This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Email cleaners always felt like a black box. Upload a list, get a number back, no idea what got tossed or why. DataBridge runs 8 validators on every address - syntax, MX, typos, role accounts, disposables, duplicates, no-reply, risky TLDs. Each rejection tells you why. Plus auto-repair: gmial.com becomes gmail.com in one click, instead of just being deleted. Saves 3-8% of typical lists. Solo founder, 8 months in. Free: 1,000 rows/month, no card. Pro: $29/mo for 100k rows.
I'm Damian, Solo founder, 8 months building DataBridge on nights and weekends.
Why I built it
kept seeing email lists where 10-20% of the rows were broken. Typos like gmial.com, role accounts, disposables, dead domains. The cleaners I tried treated it as a black box - upload a list, get back "84% valid!" and a bill. No reasoning, no way to fix what was fixable. I wanted one that showed its work.
What it does
Runs 8 validators per address: syntax, MX records (does the domain actually have a mail server?), typo detection, role accounts, disposables, duplicate detection, no-reply patterns, risky TLDs. Each rejection comes with a sourced reason so you can see exactly why before deciding what to do.
The part I'm most proud of is auto-repair. Instead of throwing out gmial.com addresses, we suggest the fix (gmail.com) and you approve in one click. Recovers 3-8% of typical lists that other cleaners just delete.
Stack Next.js 15 + Tailwind v4 + Supabase, async validation worker on Railway, Paddle for billing. The validation logic was the easy part. CSV parsing nearly killed me - BOM markers, mixed encodings, escaped commas, header rows that aren't really header rows. Rewrote the parser 3 times before it stopped crashing on real customer files.
Pricing
Free tier 1,000 rows/month no card. Pro $29/mo for 100k rows + auto-repair.
Brutal feedback welcome, especially from anyone running newsletters or B2B email. Two things I'd love feedback on: what audit fields you'd want that I don't have yet, and what would actually make you trust an "invalid" verdict enough to delete the row.
(Especially curious about the second one - it's the trust gap I haven't fully figured out yet.)
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About DataBridge on Product Hunt
“Email cleaning that shows its work”
DataBridge was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #96 on the daily leaderboard. Email cleaners always felt like a black box. Upload a list, get a number back, no idea what got tossed or why. DataBridge runs 8 validators on every address - syntax, MX, typos, role accounts, disposables, duplicates, no-reply, risky TLDs. Each rejection tells you why. Plus auto-repair: gmial.com becomes gmail.com in one click, instead of just being deleted. Saves 3-8% of typical lists. Solo founder, 8 months in. Free: 1,000 rows/month, no card. Pro: $29/mo for 100k rows.
DataBridge was featured in Email (36.7k followers), Email Marketing (33.4k followers) and SaaS (42.2k followers) on Product Hunt. Together, these topics include over 55.2k products, making this a competitive space to launch in.
Who hunted DataBridge?
DataBridge was hunted by Damian Forzani. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how DataBridge stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Damian Forzani
Hey PH 👋
I'm Damian, Solo founder, 8 months building DataBridge on nights and weekends.
Why I built it
kept seeing email lists where 10-20% of the rows were broken. Typos like gmial.com, role accounts, disposables, dead domains. The cleaners I tried treated it as a black box - upload a list, get back "84% valid!" and a bill. No reasoning, no way to fix what was fixable. I wanted one that showed its work.
What it does
Runs 8 validators per address: syntax, MX records (does the domain actually have a mail server?), typo detection, role accounts, disposables, duplicate detection, no-reply patterns, risky TLDs. Each rejection comes with a sourced reason so you can see exactly why before deciding what to do.
The part I'm most proud of is auto-repair. Instead of throwing out gmial.com addresses, we suggest the fix (gmail.com) and you approve in one click. Recovers 3-8% of typical lists that other cleaners just delete.
Stack
Next.js 15 + Tailwind v4 + Supabase, async validation worker on Railway, Paddle for billing. The validation logic was the easy part. CSV parsing nearly killed me - BOM markers, mixed encodings, escaped commas, header rows that aren't really header rows. Rewrote the parser 3 times before it stopped crashing on real customer files.
Pricing
Free tier 1,000 rows/month no card. Pro $29/mo for 100k rows + auto-repair.
Brutal feedback welcome, especially from anyone running newsletters or B2B email. Two things I'd love feedback on: what audit fields you'd want that I don't have yet, and what would actually make you trust an "invalid" verdict enough to delete the row.
(Especially curious about the second one - it's the trust gap I haven't fully figured out yet.)
Thanks for reading 🙏